//function to calc Mann-Whitney statistics
private double[] calcMWStats()
{
double[] stats = new double[8]; //to store final stats
List <double> stoichsG1 = new List <double>(); //double[(this.PeptideStoichiometriesGroupOne.Count()]; //list to store distribution of group one stoichiometries
List <double> stoichsG2 = new List <double>(); //double[(this.PeptideStoichiometriesGroupTwo.Count()]; //list to store distribution of group two stoichiometries
foreach (Stoichiometry S1 in (this.PeptideStoichiometriesGroupOne))
{
stoichsG1.Add(S1.StoichiometryVal);
}
foreach (Stoichiometry S2 in (this.PeptideStoichiometriesGroupTwo))
{
stoichsG2.Add(S2.StoichiometryVal);
}
//calc stats
TestResult mw = Univariate.MannWhitneyTest(stoichsG1, stoichsG2);
stats[0] = mw.Statistic.Value;
stats[1] = mw.Probability;
//find medians, mins maxs
stats[2] = stoichsG1.Median();
stats[4] = stoichsG1.Min();
stats[6] = stoichsG1.Max();
stats[3] = stoichsG2.Median();
stats[5] = stoichsG2.Min();
stats[7] = stoichsG2.Max();
return(stats);
}
public void InternetSampleDownload()
{
FrameTable table = DownloadFrameTable(new Uri("https://raw.githubusercontent.com/Dataweekends/zero_to_deep_learning_udemy/master/data/weight-height.csv"));
FrameView view = table.WhereNotNull();
view.AddComputedColumn("Bmi", (FrameRow r) => {
double h = (double)r["Height"];
double w = (double)r["Weight"];
return(w / (h * h));
});
FrameView males = view.Where("Gender", (string s) => (s == "Male"));
FrameView females = view.Where("Gender", (string s) => (s == "Female"));
SummaryStatistics maleSummary = new SummaryStatistics(males["Height"].As <double>());
SummaryStatistics femaleSummary = new SummaryStatistics(females["Height"].As <double>());
TestResult allNormal = view["Height"].As <double>().ShapiroFranciaTest();
TestResult maleNormal = males["Height"].As <double>().ShapiroFranciaTest();
TestResult femaleNormal = females["Height"].As <double>().ShapiroFranciaTest();
TestResult tTest = Univariate.StudentTTest(males["Height"].As <double>(), females["Height"].As <double>());
TestResult mwTest = Univariate.MannWhitneyTest(males["Height"].As <double>(), females["Height"].As <double>());
LinearRegressionResult result0 = males["Weight"].As <double>().LinearRegression(males["Height"].As <double>());
PolynomialRegressionResult result1 = males["Height"].As <double>().PolynomialRegression(males["Weight"].As <double>(), 1);
PolynomialRegressionResult result2 = males["Height"].As <double>().PolynomialRegression(males["Weight"].As <double>(), 2);
PolynomialRegressionResult result3 = males["Height"].As <double>().PolynomialRegression(males["Weight"].As <double>(), 3);
//MultiLinearRegressionResult multi = view["Weight"].As<double>().MultiLinearRegression(view["Height"].As<double>(), view["Gender"].As<string>().Select(s => (s == "Male") ? 1.0 : 0.0).ToList());
}
private static void CompareSamples()
{
List <double> a = new List <double>()
{
130.0, 140.0, 150.0, 150.0, 160.0, 190.0
};
List <double> b = new List <double>()
{
120.0, 150.0, 180.0, 170.0, 185.0, 175.0, 190.0, 200.0
};
TestResult student = Univariate.StudentTTest(a, b);
Console.WriteLine($"{student.Statistic.Name} = {student.Statistic.Value}");
Console.WriteLine($"{student.Type} P = {student.Probability}");
student.Type = TestType.LeftTailed;
Console.WriteLine($"{student.Type} P = {student.Probability}");
TestResult mannWhitney = Univariate.MannWhitneyTest(a, b);
Console.WriteLine($"{mannWhitney.Statistic.Name} = {mannWhitney.Statistic.Value}");
Console.WriteLine($"{mannWhitney.Type} P = {mannWhitney.Probability}");
TestResult kolmogorov = Univariate.KolmogorovSmirnovTest(a, b);
Console.WriteLine($"{kolmogorov.Statistic.Name} = {kolmogorov.Statistic.Value}");
Console.WriteLine($"{kolmogorov.Type} P = {kolmogorov.Probability}");
}